Recent displacement trends show growing numbers of displaced population living outside of designated areas such as camp/camp like setting (traditional camps collective/transit/reception centres, informal settlements) with a majority setting in dispersed locations predominately urban and peri-urban areas such as informal settlements, unfinished buildings or interspersed in host community homes and communities, shared rooms or rental arrangements. To be able to reach, properly assess and understand local dynamics, vulnerabilities and capacities of the displaced and host populations alike, humanitarian organisations are increasingly using sub-national Area Based Approach. Area based approaches define “an area, rather than a sector or target group, as a primary entry point”. Such approach is particularly appropriate when residents in an affected area face complex, inter-related and multisectoral needs, resulting in risk of forced displacement.
In the context of migration statistics, forced displacement is often analyzed with the prism of push and pull factors.
| Push Factor (Mitigated by intervention to address root causes within countries of origin) | Pull Factor (Mitigated by migration & Asylum policies of receiving countries) | |
|---|---|---|
| Economic Dimension (Addressed by programme in relation with development & poverty alleviation) | Lack of public services, Unemployement, Overpopulation | More jobs, Better jobs, Higher wages, promise of a “better individual life” |
| Social Dimension (Addressed by programme in relation with protection) | Violence, insecurity, intolerance towards certain groups, active political or religious persecution, | Safety, tolerance, freedom |
| Environmental Dimension (Addressed by programme in relation with resilience & sustainability) | Climate change, natural disasters | More livable environment |
Though, traditional statistical data sources are often lacking sufficient geographically-fine-grained disaggregation to inform sub national scale approach and characterization. Alternative based on sophisticated index like Inform Colombia requires extensive expert consultations and might not fully reflect the important dimension to be reflected in the context of forced displacement and migration.
New sensors provide unique abilities to capture new flow of information from social medias (Anonymized data from Facebook platform) at subnational scale through grid level information. Satellite data can pick up signals of economic activity by detecting light at night, it can pick up development status by detecting infrastructure such as roads, and it can pick up signals for individual household wealth by detecting different building footprints and roof types.
In regard to the framework above, an initial selection of globally available layers includes:
Information can be compiled and aggregated at admin level 2 in order to build composite Indicators. Different areas can be then grouped together based on the values from those composite indicators. The advantage of this approach are multiple: 1. Granularity: Optimal Level of granularity 2. Availibility: Data Consistently and freely available worldwide, simplicity to obtain information, ensor based indicators are potentially less sensitive to political pressure 3. Reproducibility: Can be used in multiple countries easily and Fully automated and audited through reproducible analysis script
The resulting information can complement other traditional source of information both on quantitative (Household Survey) and qualitative (Focus Group Discussions) side.
Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty.
As explained in a dedicated paper, the Relative Wealth Index estimates are built by applying machine learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, topographic maps, as well as aggregated and de-identified connectivity data from Facebook.
As described in the this tutorial
Determine which administrative unit contains the centroid of each RWI tile
Calculate the bing tile quadkey at zoom level 14 for each point in the population density dataset and sum the population per level 14 tile
Determine which zoom level 14 (~2.4km bing tile) corresponds to each of the smaller 30m population density tiles, and calculate the sum of population within each zoom level 14 tile.
Calculate the total population in each administrative region using the population density dataset
Calculate a population derived weight for each zoom level 14 RWI tile
Use the weight value to calculate a weighted RWI value and aggregate to the administrative unit level
Social:
Population Dependency Ratio (Facebook)
Population movement range
The Movement Range data sets is intended to inform on how populations are responding to physical distancing measures. In particular, there are two metrics that provide a slightly different perspective on movement trends:
Change in Movement: looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures. The idea is to understand how much less people are moving around since the onset of the coronavirus epidemic. This is done by quantifying how much people move around by counting the number of level-16 Bing tiles (which are approximately 600 meters by 600 meters in area at the equator) they are seen in within a day. In the dataset noted
all_day_bing_tiles_visited_relative_changeStay Put: looks at the fraction of the population that appear to stay within a small area during an entire day. This metric intends to measure this by calculating the percentage of eligible people who are only observed in a single level-16 Bing tile during the course of a day. In the dataset noted
all_day_ratio_single_tile_usersViolence